Tissue Biology: In Search of a New Paradigm.

Animal tissues are made up of multiple cell types that are increasingly well-characterized, yet our understanding of the core principles that govern tissue organization is still incomplete. This is in part because many observable tissue characteristics, such as cellular composition and spatial patterns, are emergent properties, and as such, they cannot be explained through the knowledge of individual cells alone. Here we propose a complex systems theory perspective to address this fundamental gap in our understanding of tissue biology. We introduce the concept of cell categories, which is based on cell relations rather than cell identity. Based on these notions we then discuss common principles of tissue modularity, introducing compositional, structural, and functional tissue modules. Cell diversity and cell relations provide a basis for a new perspective on the underlying principles of tissue organization in health and disease.

Comparative analysis of gene regulatory networks: from network reconstruction to evolution.

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

Neural network architecture of a mammalian brain.

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Chromatin modules and their implication in genomic organization and gene regulation.

Regulation of gene expression is a complex but highly guided process. While genomic technologies and computational approaches have allowed high-throughput mapping of cis-regulatory elements (CREs) and their interactions in 3D, their precise role in regulating gene expression remains obscure. Recent complementary observations revealed that interactions between CREs frequently result in the formation of small-scale functional modules within topologically associating domains. Such chromatin modules likely emerge from a complex interplay between regulatory machineries assembled at CREs, including site-specific binding of transcription factors. Here, we review the methods that allow identifying chromatin modules, summarize possible mechanisms that steer CRE interactions within these modules, and discuss outstanding challenges to uncover how chromatin modules fit in our current understanding of the functional 3D genome.

Reframing macrophage diversity with network motifs.

A binary classification of macrophage activation as inflammatory or resolving does not capture the diversity of macrophage states observed in tissues. However, framing macrophage activation as a continuous spectrum of states overlooks the intracellular and extracellular networks that regulate and coordinate macrophage responses. Here, we suggest that the systems biology concept of network motifs, which incorporate rules of local molecular interactions, is useful for reframing macrophage activation. Because network motifs can be used to regulate distinct biological functions, they offer a simplified unit that can be compared across organismal, tissue, and disease contexts. Moreover, defining macrophage states as combinations of functional modules regulated by network motifs offers a framework to ultimately predict and target macrophage responses arising in complex environments.

Toxins, Targets, and Triggers: An Overview of Toxin-Antitoxin Biology.

Bacterial toxin-antitoxin (TA) modules are abundant genetic elements that encode a toxin protein capable of inhibiting cell growth and an antitoxin that counteracts the toxin. The majority of toxins are enzymes that interfere with translation or DNA replication, but a wide variety of molecular activities and cellular targets have been described. Antitoxins are proteins or RNAs that often control their cognate toxins through direct interactions and, in conjunction with other signaling elements, through transcriptional and translational regulation of TA module expression. Three major biological functions of TA modules have been discovered, post-segregational killing ("plasmid addiction"), abortive infection (bacteriophage immunity through altruistic suicide), and persister formation (antibiotic tolerance through dormancy). In this review, we summarize the current state of the field and highlight how multiple levels of regulation shape the conditions of toxin activation to achieve the different biological functions of TA modules.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways.

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

Patterns of Fitness and Gene Expression Epistasis Generated by Beneficial Mutations in the rho and rpoB Genes of Escherichia coli during High-Temperature Adaptation.

Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2 °C). The double mutants exhibited patterns of fitness epistasis that included diminishing returns epistasis at 42.2 °C, stronger diminishing returns between mutations with larger beneficial effects and both negative and positive (sign) epistasis across environments (20.0 °C and 37.0 °C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly connected hub genes in coexpression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both coexpression and protein-protein interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination but that was not the case. Altogether, gene expression and coexpression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression toward the unstressed state.

Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity.

As microRNAs (miRNAs) are involved in many essential biological processes, their abnormal expressions can serve as biomarkers and prognostic indicators to prevent the development of complex diseases, thus providing accurate early detection and prognostic evaluation. Although a number of computational methods have been proposed to predict miRNA-disease associations (MDAs) for further experimental verification, their performance is limited primarily by the inadequacy of exploiting lower order patterns characterizing known MDAs to identify missing ones from MDA networks. Hence, in this work, we present a novel prediction model, namely HiSCMDA, by incorporating higher order network structures for improved performance of MDA prediction. To this end, HiSCMDA first integrates miRNA similarity network, disease similarity network and MDA network to preserve the advantages of all these networks. After that, it identifies overlapping functional modules from the integrated network by predefining several higher order connectivity patterns of interest. Last, a path-based scoring function is designed to infer potential MDAs based on network paths across related functional modules. HiSCMDA yields the best performance across all datasets and evaluation metrics in the cross-validation and independent validation experiments. Furthermore, in the case studies, 49 and 50 out of the top 50 miRNAs, respectively, predicted for colon neoplasms and lung neoplasms have been validated by well-established databases. Experimental results show that rich higher order organizational structures exposed in the MDA network gain new insight into the MDA prediction based on higher order connectivity patterns.

Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data.

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

Correlated gene modules uncovered by high-precision single-cell transcriptomics.

Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.

Comparative proteomic analysis across the developmental stages of the Eimeria tenella.

Eimeria tenella is the main pathogen responsible for coccidiosis in chickens. The life cycle of E. tenella is, arguably, the least complex of all Coccidia, with only one host. However, it presents different developmental stages, either in the environment or in the host and either intracellular or extracellular. Its signaling and metabolic pathways change with its different developmental stages. Until now, little is known about the developmental regulation and transformation mechanisms of its life cycle. In this study, protein profiles from the five developmental stages, including unsporulated oocysts (USO), partially sporulated (7 h) oocysts (SO7h), sporulated oocysts (SO), sporozoites (S) and second-generation merozoites (M2), were harvested using the label-free quantitative proteomics approach. Then the differentially expressed proteins (DEPs) for these stages were identified. A total of 314, 432, 689, and 665 DEPs were identified from the comparison of SO7h vs USO, SO vs SO7h, S vs SO, and M2 vs S, respectively. By conducting weighted gene coexpression network analysis (WGCNA), six modules were dissected. Proteins in blue and brown modules were calculated to be significantly positively correlated with the E. tenella developmental stages of sporozoites (S) and second-generation merozoites (M2), respectively. In addition, hub proteins with high intra-module degree were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyses revealed that hub proteins in blue modules were involved in electron transport chain and oxidative phosphorylation. Hub proteins in the brown module were involved in RNA splicing. These findings provide new clues and ideas to enhance our fundamental understanding of the molecular mechanisms underlying parasite development.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

BACKGROUND: With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed. RESULTS: Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task. CONCLUSIONS: The inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

BSXplorer: analytical framework for exploratory analysis of BS-seq data.

BACKGROUND: Bisulfite sequencing detects and quantifies DNA methylation patterns, contributing to our understanding of gene expression regulation, genome stability maintenance, conservation of epigenetic mechanisms across divergent taxa, epigenetic inheritance and, eventually, phenotypic variation. Graphical representation of methylation data is crucial in exploring epigenetic regulation on a genome-wide scale in both plants and animals. This is especially relevant for non-model organisms with poorly annotated genomes and/or organisms where genome sequences are not yet assembled on chromosome level. Despite being a technology of choice to profile DNA methylation for many years now there are surprisingly few lightweight and robust standalone tools available for efficient graphical analysis of data in non-model systems. This significantly limits evolutionary studies and agrigenomics research. BSXplorer is a tool specifically developed to fill this gap and assist researchers in explorative data analysis and in visualising and interpreting bisulfite sequencing data more easily. RESULTS: BSXplorer provides in-depth graphical analysis of sequencing data encompassing (a) profiling of methylation levels in metagenes or in user-defined regions using line plots and heatmaps, generation of summary statistics charts, (b) enabling comparative analyses of methylation patterns across experimental samples, methylation contexts and species, and (c) identification of modules sharing similar methylation signatures at functional genomic elements. The tool processes methylation data quickly and offers API and CLI capabilities, along with the ability to create high-quality figures suitable for publication. CONCLUSIONS: BSXplorer facilitates efficient methylation data mining, contrasting and visualization, making it an easy-to-use package that is highly useful for epigenetic research.

Identification and structural analysis of a carbohydrate-binding module specific to alginate, a representative of a new family, CBM96.

Carbohydrate-binding modules (CBMs) are the noncatalytic modules that assist functions of the catalytic modules in carbohydrate-active enzymes, and they are usually discrete structural domains in larger multimodular enzymes. CBMs often occur in tandem in different alginate lyases belonging to the CBM families 13, 16, and 32. However, none of the currently known CBMs in alginate lyases specifically bind to an internal alginate chain. In our investigation of the multidomain alginate lyase Dp0100 carrying several ancillary domains, we identified an alginate-binding domain denoted TM6-N4 using protein truncation analysis. The structure of this CBM domain was determined at 1.35 Å resolution. TM6-N4 exhibited an overall ß-sandwich fold architecture with two antiparallel ß-sheets. We identified an extended binding groove in the CBM using site-directed mutagenesis, docking, and surface electrostatic potential analysis. Affinity analysis revealed that residues of Lys10, Lys22, Lys25, Lys27, Lys31, Arg36, and Tyr159 located on the bottom or the wall of the shallow groove are responsible for alginate binding, and isothermal titration calorimetry analyses indicated that the binding cleft consists of six subsites for sugar recognition. This substrate binding pattern is typical for type B CBM, and it represents the first CBM domain that specifically binds internal alginate chain. Phylogenetic analysis supports that TM6-N4 constitutes the founding member of a new CBM family denoted as CBM96. Our reported structure not only facilitates the investigation of the CBM-alginate ligand recognition mechanism but also inspires the utilization of the CBM domain in biotechnical applications.

Ectomycorrhizal symbiosis prepares its host locally and systemically for abiotic cue signaling.

Tree growth and survival are dependent on their ability to perceive signals, integrate them, and trigger timely and fitted molecular and growth responses. While ectomycorrhizal symbiosis is a predominant tree-microbe interaction in forest ecosystems, little is known about how and to what extent it helps trees cope with environmental changes. We hypothesized that the presence of Laccaria bicolor influences abiotic cue perception by Populus trichocarpa and the ensuing signaling cascade. We submitted ectomycorrhizal or non-ectomycorrhizal P. trichocarpa cuttings to short-term cessation of watering or ozone fumigation to focus on signaling networks before the onset of any physiological damage. Poplar gene expression, metabolite levels, and hormone levels were measured in several organs (roots, leaves, mycorrhizas) and integrated into networks. We discriminated the signal responses modified or maintained by ectomycorrhization. Ectomycorrhizas buffered hormonal changes in response to short-term environmental variations systemically prepared the root system for further fungal colonization and alleviated part of the root abscisic acid (ABA) signaling. The presence of ectomycorrhizas in the roots also modified the leaf multi-omics landscape and ozone responses, most likely through rewiring of the molecular drivers of photosynthesis and the calcium signaling pathway. In conclusion, P. trichocarpa-L. bicolor symbiosis results in a systemic remodeling of the host's signaling networks in response to abiotic changes. In addition, ectomycorrhizal, hormonal, metabolic, and transcriptomic blueprints are maintained in response to abiotic cues, suggesting that ectomycorrhizas are less responsive than non-mycorrhizal roots to abiotic challenges.

Investigating diversity and similarity between CBM13 modules and ricin-B lectin domains using sequence similarity networks.

BACKGROUND: The CBM13 family comprises carbohydrate-binding modules that occur mainly in enzymes and in several ricin-B lectins. The ricin-B lectin domain resembles the CBM13 module to a large extent. Historically, ricin-B lectins and CBM13 proteins were considered completely distinct, despite their structural and functional similarities. RESULTS: In this data mining study, we investigate structural and functional similarities of these intertwined protein groups. Because of the high structural and functional similarities, and differences in nomenclature usage in several databases, confusion can arise. First, we demonstrate how public protein databases use different nomenclature systems to describe CBM13 modules and putative ricin-B lectin domains. We suggest the introduction of a novel CBM13 domain identifier, as well as the extension of CAZy cross-references in UniProt to guard the distinction between CAZy and non-CAZy entries in public databases. Since similar problems may occur with other lectin families and CBM families, we suggest the introduction of novel CBM InterPro domain identifiers to all existing CBM families. Second, we investigated phylogenetic, nomenclatural and structural similarities between putative ricin-B lectin domains and CBM13 modules, making use of sequence similarity networks. We concluded that the ricin-B/CBM13 superfamily may be larger than initially thought and that several putative ricin-B lectin domains may display CAZyme functionalities, although biochemical proof remains to be delivered. CONCLUSIONS: Ricin-B lectin domains and CBM13 modules are associated groups of proteins whose database semantics are currently biased towards ricin-B lectins. Revision of the CAZy cross-reference in UniProt and introduction of a dedicated CBM13 domain identifier in InterPro may resolve this issue. In addition, our analyses show that several proteins with putative ricin-B lectin domains show very strong structural similarity to CBM13 modules. Therefore ricin-B lectin domains and CBM13 modules could be considered distant members of a larger ricin-B/CBM13 superfamily.

Body size and trophic position determine the outcomes of species invasions along temperature and productivity gradients.

Species invasions are predicted to increase in frequency with global change, but quantitative predictions of how environmental filters and species traits influence the success and consequences of invasions for local communities are lacking. Here we investigate how invaders alter the structure, diversity and stability regime of simple communities across environmental gradients (habitat productivity, temperature) and community size structure. We simulate all three-species trophic modules (apparent and exploitative competition, trophic chain and intraguild predation). We predict that invasions most often succeed in warm and productive habitats and that successful invaders include smaller competitors, intraguild predators and comparatively small top predators. This suggests that species invasions and global change may facilitate the downsizing of food webs. Furthermore, we show that successful invasions leading to species substitutions rarely alter system stability, while invasions leading to increased diversity can destabilize or stabilize community dynamics depending on the environmental conditions and invader's trophic position.